Issue link: https://resources.mouser.com/i/1442826
24 AI Excelling at AI Requires Powerful Hardware and a Strong Software Ecosystem By Joseph Byrne, Senior Strategic Marketing Manager, NXP Digital Networking Group lthough known for their networking prowess, NXP's Layerscape processors are gaining traction in artificial intelligence (AI) applications. These applications include security and surveillance, home and building automation, factory safety, and machine inspection. The reason is that Layerscape's connectivity and general-purpose processing enable these processors to address applications where wired and wireless communications is a crucial requirement, and powerful multicore CPUs can tackle multiple computationally intensive tasks. For those surprised that the networking-centric Layerscape family deserves consideration for AI designs, I've got news. Layerscape executes AI algorithms quite well, and it's a good fit for a lot of plans. On the hardware side, Layerscape combines either the efficient Cortex-A53 or the powerful Cortex-A72 CPUs from Arm with sizeable caches and DRAM bandwidth. Figure 1 shows how critical functions in a design using Layerscape for AI-based image processing can map to a Layerscape LS1043A or LS1046A processor. Cameras and radar sensors connect via USB or Ethernet. Ethernet can also connect to a WAN uplink and the LAN (also available via PCIe- connected Wi-Fi) if this system is an edge gateway. The four CPUs handle application logic, networking functions, capture of camera and radar data, and AI-based classification of this data. The software side is at least as necessary. Frameworks— software libraries for AI-related numerical computation— optimized for mobile and embedded devices instead of servers are coming to market, enabling performance increases. These include open-source frameworks, such as Google's TensorFlow Lite™ and Tencent's NCNN™, and commercial engines like DeepView™ from Au-Zone. By optimizing models through judicious pruning (eliminating less-useful neural-network parameters) and quantization (e.g., mapping floating-point value to eight-bit integers), these frameworks reduce memory and computation required to crunch models. In the case of video analysis, faster performance can be seen in 5-10x gains in frames per second. A ❝ ❞ The high-performance processing and I/O of the Layerscape family paired with optimized software makes excellence with AI a reality.